All innovators and seekers of new data-driven services are searching for ideas that have 4 characteristics:
› Data driven service has value for the user of the data, in other terms he needs to be ready either to pay for the data or to exchange something that has value for the seller of the data;
› Data driven service needs to be viable in the long run, as many data based services require development and investment, the business model needs to be sustainable in the long term;
› Data driven services need to be lawful and respectful of data rights therefore requiring to pro tect user and provider rights;
› Data driven services need to be feasible, both technologically but also from a quantitative and qualitative standpoint
Therefore a 3-step approach is recommended:
1. Alignment around a common understanding of which industry trends we want to capitalize,
exploring the ecosystem or adjacent businesses and identifying new value generators.
2. On the other end, generating ideas around new business models of the data users or clients requiring access to data.
3. Acceleration and actionable plans by demonstrating the technology with prototypes, MVPs
and business modeling.
Data based service models require to understand the data value chain
All innovators and seekers of new data-driven services are searching for ideas that have 4 characteristics:
› Data driven service has value for the user of the data, in other terms he needs to be ready either to pay for the data or to exchange something that has value for the seller of the data;
› Data driven service needs to be viable in the long run, as many data based services require development and investment, the business model needs to be sustainable in the long term;
› Data driven services need to be lawful and respectful of data rights therefore requiring to pro tect user and provider rights;
› Data driven services need to be feasible, both technologically but also from a quantitative and qualitative standpoint
Therefore a 3-step approach is recommended:
1. Alignment around a common understanding of which industry trends we want to capitalize,
exploring the ecosystem or adjacent businesses and identifying new value generators.
2. On the other end, generating ideas around new business models of the data users or clients requiring access to data.
3. Acceleration and actionable plans by demonstrating the technology with prototypes, MVPs
and business modeling.
Alignment around a common understanding
Wich data:
› People
› Things
› Flow
Which industry trend:
› Shared, social and sustainable economy
› Remote operations, service and outsourcing
› Grid, communities, crowd funding
Which expected benefits:
› Redefining user experience
› Personalization of service
› Frictionless, one stop shop
› Prediction, statistics based on rich data
Generating ideas around new business models
Business model
› How do you make money
› What is being exchanged or traded
› How do you close the business loop
Business flows
› Value proposition
› Commununity and client access
› Personas and client segments
› Partners and data exchange potential
Business flows
› Pay per use
› Pay per time
› Data against services
› Freemium
› Pay what you want
› Yield based pricing
› Etc.
Acceleration by demonstrating MVP
Choose a real concrete use case based on the business model chosen
Set a reasonable time frame to keep momentum and test the idea
Define clear deliverables and expectations
Involve the company, its capabilities to produce the data or to process data, its current processes and its people
Stay focused on the data model you are trying to test
Data protection and security: essential to bring trust
Implement measures against data fraud
Protect personal data against attacks
Ensure portability security (gdpr)
Protect exchange and flows of data on the move (hacking)
Alignment around a common understanding
Wich data:
› People
› Things
› Flow
Which industry trend:
› Shared, social and sustainable economy
› Remote operations, service and outsourcing
› Grid, communities, crowd funding
Which expected benefits:
› Redefining user experience
› Personalization of service
› Frictionless, one stop shop
› Prediction, statistics based on rich data
Generating ideas around new business models
Business model
› How do you make money
› What is being exchanged or traded
› How do you close the business loop
Business flows
› Value proposition
› Commununity and client access
› Personas and client segments
› Partners and data exchange potential
Business flows
› Pay per use
› Pay per time
› Data against services
› Freemium
› Pay what you want
› Yield based pricing
› Etc.
Acceleration by demonstrating MVP
Choose a real concrete use case based on the business model chosen
Set a reasonable time frame to keep momentum and test the idea
Define clear deliverables and expectations
Involve the company, its capabilities to produce the data or to process data, its current processes and its people
Stay focused on the data model you are trying to test
Data protection and security: essential to bring trust
Implement measures against data fraud
Protect personal data against attacks
Ensure portability security (gdpr)
Protect exchange and flows of data on the move (hacking)
CCTV BECOMES COMPUTER VISION
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DIGITAL IDENTITY IS AN E-SERVICE ENABLER IF HANDLED WELL
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CCTV BECOMES COMPUTER VISION
With the advent of IP cameras, deep learning and machine learning as well as stability and security of networks, the way to process images has adopted artificial intelligence and neuronal networks.
